from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-30 14:09:57.706139
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 30, Dec, 2020
Time: 14:10:01
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.2962
Nobs: 156.000 HQIC: -45.3411
Log likelihood: 1690.16 FPE: 9.97575e-21
AIC: -46.0557 Det(Omega_mle): 5.70288e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.458565 0.159429 2.876 0.004
L1.Burgenland 0.139597 0.081360 1.716 0.086
L1.Kärnten -0.234033 0.065393 -3.579 0.000
L1.Niederösterreich 0.117714 0.189244 0.622 0.534
L1.Oberösterreich 0.254210 0.161927 1.570 0.116
L1.Salzburg 0.171651 0.083939 2.045 0.041
L1.Steiermark 0.082751 0.116196 0.712 0.476
L1.Tirol 0.147242 0.077740 1.894 0.058
L1.Vorarlberg 0.002746 0.074833 0.037 0.971
L1.Wien -0.123950 0.156521 -0.792 0.428
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.512274 0.206087 2.486 0.013
L1.Burgenland 0.014958 0.105170 0.142 0.887
L1.Kärnten 0.364535 0.084531 4.312 0.000
L1.Niederösterreich 0.131392 0.244628 0.537 0.591
L1.Oberösterreich -0.191569 0.209317 -0.915 0.360
L1.Salzburg 0.190375 0.108504 1.755 0.079
L1.Steiermark 0.250911 0.150202 1.670 0.095
L1.Tirol 0.141737 0.100492 1.410 0.158
L1.Vorarlberg 0.183408 0.096733 1.896 0.058
L1.Wien -0.581748 0.202328 -2.875 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.292840 0.069195 4.232 0.000
L1.Burgenland 0.107641 0.035311 3.048 0.002
L1.Kärnten -0.025940 0.028382 -0.914 0.361
L1.Niederösterreich 0.070844 0.082135 0.863 0.388
L1.Oberösterreich 0.290172 0.070279 4.129 0.000
L1.Salzburg -0.004236 0.036431 -0.116 0.907
L1.Steiermark -0.021015 0.050431 -0.417 0.677
L1.Tirol 0.088292 0.033741 2.617 0.009
L1.Vorarlberg 0.129237 0.032479 3.979 0.000
L1.Wien 0.078650 0.067933 1.158 0.247
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.202206 0.080301 2.518 0.012
L1.Burgenland -0.010101 0.040979 -0.246 0.805
L1.Kärnten 0.021202 0.032937 0.644 0.520
L1.Niederösterreich 0.025459 0.095319 0.267 0.789
L1.Oberösterreich 0.409535 0.081560 5.021 0.000
L1.Salzburg 0.098509 0.042278 2.330 0.020
L1.Steiermark 0.181856 0.058526 3.107 0.002
L1.Tirol 0.032743 0.039156 0.836 0.403
L1.Vorarlberg 0.099044 0.037692 2.628 0.009
L1.Wien -0.061652 0.078836 -0.782 0.434
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.590698 0.167330 3.530 0.000
L1.Burgenland 0.066411 0.085391 0.778 0.437
L1.Kärnten 0.003543 0.068634 0.052 0.959
L1.Niederösterreich -0.046227 0.198622 -0.233 0.816
L1.Oberösterreich 0.158538 0.169952 0.933 0.351
L1.Salzburg 0.054306 0.088099 0.616 0.538
L1.Steiermark 0.113042 0.121954 0.927 0.354
L1.Tirol 0.213881 0.081593 2.621 0.009
L1.Vorarlberg 0.008293 0.078541 0.106 0.916
L1.Wien -0.145165 0.164277 -0.884 0.377
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157229 0.116875 1.345 0.179
L1.Burgenland -0.025871 0.059644 -0.434 0.664
L1.Kärnten -0.013345 0.047939 -0.278 0.781
L1.Niederösterreich 0.175867 0.138733 1.268 0.205
L1.Oberösterreich 0.395429 0.118707 3.331 0.001
L1.Salzburg -0.028403 0.061534 -0.462 0.644
L1.Steiermark -0.047062 0.085182 -0.552 0.581
L1.Tirol 0.188329 0.056990 3.305 0.001
L1.Vorarlberg 0.042329 0.054859 0.772 0.440
L1.Wien 0.163671 0.114743 1.426 0.154
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.226550 0.146052 1.551 0.121
L1.Burgenland 0.071205 0.074533 0.955 0.339
L1.Kärnten -0.045218 0.059906 -0.755 0.450
L1.Niederösterreich -0.027309 0.173366 -0.158 0.875
L1.Oberösterreich -0.108455 0.148341 -0.731 0.465
L1.Salzburg 0.005617 0.076896 0.073 0.942
L1.Steiermark 0.381834 0.106447 3.587 0.000
L1.Tirol 0.521951 0.071218 7.329 0.000
L1.Vorarlberg 0.205655 0.068554 3.000 0.003
L1.Wien -0.229432 0.143388 -1.600 0.110
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.116603 0.170809 0.683 0.495
L1.Burgenland 0.019502 0.087167 0.224 0.823
L1.Kärnten -0.117286 0.070061 -1.674 0.094
L1.Niederösterreich 0.215307 0.202753 1.062 0.288
L1.Oberösterreich 0.004110 0.173486 0.024 0.981
L1.Salzburg 0.225403 0.089930 2.506 0.012
L1.Steiermark 0.141495 0.124490 1.137 0.256
L1.Tirol 0.090535 0.083290 1.087 0.277
L1.Vorarlberg 0.026803 0.080175 0.334 0.738
L1.Wien 0.289328 0.167693 1.725 0.084
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.582702 0.094221 6.184 0.000
L1.Burgenland -0.019310 0.048083 -0.402 0.688
L1.Kärnten 0.000656 0.038647 0.017 0.986
L1.Niederösterreich -0.009809 0.111841 -0.088 0.930
L1.Oberösterreich 0.278695 0.095697 2.912 0.004
L1.Salzburg 0.010522 0.049607 0.212 0.832
L1.Steiermark 0.001019 0.068671 0.015 0.988
L1.Tirol 0.078570 0.045944 1.710 0.087
L1.Vorarlberg 0.173895 0.044225 3.932 0.000
L1.Wien -0.092297 0.092502 -0.998 0.318
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.141817 -0.002879 0.206533 0.246890 0.060228 0.098760 -0.080122 0.162845
Kärnten 0.141817 1.000000 -0.009341 0.182930 0.132235 -0.146911 0.171090 0.026262 0.296993
Niederösterreich -0.002879 -0.009341 1.000000 0.260193 0.082528 0.199038 0.091553 0.035952 0.350199
Oberösterreich 0.206533 0.182930 0.260193 1.000000 0.277570 0.290414 0.102902 0.065397 0.104707
Salzburg 0.246890 0.132235 0.082528 0.277570 1.000000 0.146962 0.062867 0.080797 -0.027822
Steiermark 0.060228 -0.146911 0.199038 0.290414 0.146962 1.000000 0.098460 0.085444 -0.133209
Tirol 0.098760 0.171090 0.091553 0.102902 0.062867 0.098460 1.000000 0.127654 0.132379
Vorarlberg -0.080122 0.026262 0.035952 0.065397 0.080797 0.085444 0.127654 1.000000 0.090740
Wien 0.162845 0.296993 0.350199 0.104707 -0.027822 -0.133209 0.132379 0.090740 1.000000